Handling emotions in human‐computer dialogues has emerged as a challenging task which requires artificial intelligence systems to generate emotional responses by jointly perceiving the emotion involved in the input p...Handling emotions in human‐computer dialogues has emerged as a challenging task which requires artificial intelligence systems to generate emotional responses by jointly perceiving the emotion involved in the input posts and incorporating it into the gener-ation of semantically coherent and emotionally reasonable responses.However,most previous works generate emotional responses solely from input posts,which do not take full advantage of the training corpus and suffer from generating generic responses.In this study,we introduce a hierarchical semantic‐emotional memory module for emotional conversation generation(called HSEMEC),which can learn abstract semantic conver-sation patterns and emotional information from the large training corpus.The learnt semantic and emotional knowledge helps to enrich the post representation and assist the emotional conversation generation.Comprehensive experiments on a large real‐world conversation corpus show that HSEMEC can outperform the strong baselines on both automatic and manual evaluation.For reproducibility,we release the code and data publicly at:https://github.com/siat‐nlp/HSEMEC‐code‐data.展开更多
With the continuous improvement of the e-commerce ecosystem and the rapid growth of e-commerce data, inthe context of the e-commerce ecosystem, consumers ask hundreds of millions of questions every day. In order toimp...With the continuous improvement of the e-commerce ecosystem and the rapid growth of e-commerce data, inthe context of the e-commerce ecosystem, consumers ask hundreds of millions of questions every day. In order toimprove the timeliness of customer service responses, many systems have begun to use customer service robotsto respond to consumer questions, but the current customer service robots tend to respond to specific questions.For many questions that lack background knowledge, they can generate only responses that are biased towardsgenerality and repetitiveness. To better promote the understanding of dialogue and generate more meaningfulresponses, this paper introduces knowledge information into the research of question answering system by usinga knowledge graph. The unique structured knowledge base of the knowledge graph is convenient for knowledgequery, can acquire knowledge faster, and improves the background information needed for answering questions. Toavoid the lack of information in the dialogue process, this paper proposes the Multi-hop Knowledge InformationEnhanced Dialogue-Graph Attention (MKIED-GA) model. The model first retrieves the problem subgraph directlyrelated to the input information from the entire knowledge base and then uses the graph neural network as theknowledge inference module on the subgraph to encode the subgraph. The graph attention mechanism is usedto determine the one-hop and two-hop entities that are more relevant to the problem to achieve the aggregationof highly relevant neighbor information. This further enriches the semantic information to provide a betterunderstanding of the meaning of the input question and generate appropriate response information. In the processof generating a response, a multi-attention flow mechanism is used to focus on different information to promotethe generation of better responses. Experiments have proved that the model presented in this article can generatemore meaningful responses than other models.展开更多
针对以往医疗对话生成方法未能有效建模医学知识,导致生成的回复缺乏医学常识一贯性的问题,很多学者尝试引入医疗知识图谱,但集成医疗知识图谱时容易占用较多输入数据空间,这限制了模型输入可以保留对话上下文信息量的大小。本文提出知...针对以往医疗对话生成方法未能有效建模医学知识,导致生成的回复缺乏医学常识一贯性的问题,很多学者尝试引入医疗知识图谱,但集成医疗知识图谱时容易占用较多输入数据空间,这限制了模型输入可以保留对话上下文信息量的大小。本文提出知识嵌入的医疗对话生成模型(medical conversation generation model based on knowledge embedding,MCG-KE),该模型基于历史对话进行实体预测得到上下文知识嵌入实体,引入串行图编码方式和图注意力机制获得当前对话相关的医疗知识图谱子图编码,将上下文知识嵌入实体、医疗知识图谱子图编码和历史对话编码作为对话生成模型的输入,用于知识嵌入的医疗对话生成。实验结果表明,模型在高效计算的情况下,所生成的医疗对话在自动评价和人工评价等相关指标上的性能均有提升。展开更多
基金supported by the National Natural Science Foundation of China(No.61906185,61876053)the Natural Science Foundation of Guangdong Province of China(No.2019A1515011705 and No.2021A1515011905)+2 种基金the Youth Innovation Promotion Association of CAS China(No.2020357)the Shenzhen Basic Research Foundation(No.JCYJ20210324115614039 and No.JCYJ20200109113441941)the Shenzhen Science and Technology Innovation Program(Grant No.KQTD20190929172835662).
文摘Handling emotions in human‐computer dialogues has emerged as a challenging task which requires artificial intelligence systems to generate emotional responses by jointly perceiving the emotion involved in the input posts and incorporating it into the gener-ation of semantically coherent and emotionally reasonable responses.However,most previous works generate emotional responses solely from input posts,which do not take full advantage of the training corpus and suffer from generating generic responses.In this study,we introduce a hierarchical semantic‐emotional memory module for emotional conversation generation(called HSEMEC),which can learn abstract semantic conver-sation patterns and emotional information from the large training corpus.The learnt semantic and emotional knowledge helps to enrich the post representation and assist the emotional conversation generation.Comprehensive experiments on a large real‐world conversation corpus show that HSEMEC can outperform the strong baselines on both automatic and manual evaluation.For reproducibility,we release the code and data publicly at:https://github.com/siat‐nlp/HSEMEC‐code‐data.
基金Funder One,National Nature Science Foundation of China,Grant/Award No.61972357Funder Two,National Nature Science Foundation of China,Grant/Award No.61672337Funder Three,Guangxi Colleges and Universities Basic Ability Improvement Project of Young and Middle-Aged Teachers,Grant/Award No.2018KY0651.
文摘With the continuous improvement of the e-commerce ecosystem and the rapid growth of e-commerce data, inthe context of the e-commerce ecosystem, consumers ask hundreds of millions of questions every day. In order toimprove the timeliness of customer service responses, many systems have begun to use customer service robotsto respond to consumer questions, but the current customer service robots tend to respond to specific questions.For many questions that lack background knowledge, they can generate only responses that are biased towardsgenerality and repetitiveness. To better promote the understanding of dialogue and generate more meaningfulresponses, this paper introduces knowledge information into the research of question answering system by usinga knowledge graph. The unique structured knowledge base of the knowledge graph is convenient for knowledgequery, can acquire knowledge faster, and improves the background information needed for answering questions. Toavoid the lack of information in the dialogue process, this paper proposes the Multi-hop Knowledge InformationEnhanced Dialogue-Graph Attention (MKIED-GA) model. The model first retrieves the problem subgraph directlyrelated to the input information from the entire knowledge base and then uses the graph neural network as theknowledge inference module on the subgraph to encode the subgraph. The graph attention mechanism is usedto determine the one-hop and two-hop entities that are more relevant to the problem to achieve the aggregationof highly relevant neighbor information. This further enriches the semantic information to provide a betterunderstanding of the meaning of the input question and generate appropriate response information. In the processof generating a response, a multi-attention flow mechanism is used to focus on different information to promotethe generation of better responses. Experiments have proved that the model presented in this article can generatemore meaningful responses than other models.
文摘针对以往医疗对话生成方法未能有效建模医学知识,导致生成的回复缺乏医学常识一贯性的问题,很多学者尝试引入医疗知识图谱,但集成医疗知识图谱时容易占用较多输入数据空间,这限制了模型输入可以保留对话上下文信息量的大小。本文提出知识嵌入的医疗对话生成模型(medical conversation generation model based on knowledge embedding,MCG-KE),该模型基于历史对话进行实体预测得到上下文知识嵌入实体,引入串行图编码方式和图注意力机制获得当前对话相关的医疗知识图谱子图编码,将上下文知识嵌入实体、医疗知识图谱子图编码和历史对话编码作为对话生成模型的输入,用于知识嵌入的医疗对话生成。实验结果表明,模型在高效计算的情况下,所生成的医疗对话在自动评价和人工评价等相关指标上的性能均有提升。
